How Much GPU Memory Do You Need for CAD?

Herrera on Hardware: To answer this question, start by learning how the technology affects your workflow.

However, depending on how many GB we're talking about, the merits of a large memory footprint aren't always visible in the CAD-relevant benchmarks we look at to get an indication of a GPU's performance level. One benchmark that does stress GPU memory — to a point — is SPEC's Viewperf 12 benchmark. Viewperf renders several viewsets, and two of them (specific to Dassault Systèmes CATIA and PTC Creo) can chew up around 2 GB of GPU memory. So while Viewperf may modestly punish cards with less than 2 GB, it won't particularly reward GPUs with physical memory sizes in excess of 2 GB.

That doesn't mean a card with 3 GB or more won't deliver performance benefits, because as the model size grows, so does the consumption of GPU memory. And while running out of physical GPU memory shouldn't be catastrophic in the blue-screen sense (GPU drivers are designed to adapt when storage demands outpace capacity), it's generally a performance killer. When it comes to GPU processing, executing out of local memory delivers performance that’s orders of magnitude higher than executing piecemeal out of system memory (i.e., multiple copies of data subsets to GPU memory) — and that will directly translate to a major slowdown in rendering throughput.

Accordingly, the larger and more complex your CAD models are, the bigger your GPU's memory should be. Where a typical consumer product might only require a few hundred MB of local storage, a model of a car or aircraft could consume 8 GB or more. So while an entry- to mid-range GPU with 1 or 2 GB may be enough for the bulk of CAD use, there are always pockets of users that can benefit from more, and a few that want the most possible, period.

Ranges of typical CAD model sizes today. (Size does not include frame buffers, textures, or any other ancillary data.) Data sources: Cadalyst, Nvidia, and AMD.

Another Reason for Larger GPU Memory Sizes: GPGPU

Raster-based graphics rendering is no longer the only thing occupying GPU execution cycles and memory. General-purpose computing on GPUs, or GPGPU, represents a growing usage model, leveraging GPUs' prowess in highly parallel, floating-point intensive math. More than a handful of the most compelling uses of GPGPU computing apply to CAD and CAE.

Take raytraced renderers, for example, which are highly appealing on the styling side of the product design workflow. A raytracer prefers access to an entire scene’s dataset, fetching the appropriate object description as the ray bounces around the scene. In the worst case, local GPU memory just isn’t big enough to store all the scene’s data structures, and rendering must be unceremoniously kicked back to the host for vastly slower processing in software — no graceful degradation here.

GPU Memory Rules of Thumb

If you're pushing the envelope on model complexity, you're probably well aware of the heavy demands on all aspects of your computing hardware, GPUs included. And you're going to opt for a higher-end GPU with four, eight, or more GB of local memory. But for the majority of CAD users today, a card that offers around 2 GB of GPU memory and falls in the $200 to $500 range will hit the sweet spot in terms of both price and capacity.

Still, it's a decision you'll want to weigh in the context of tomorrow’s needs as well. Because even if your GPU gives you all you need for today's CAD project, you can’t expect things to stay that way. Inevitably, the nature of this competitive business demands you do more on the next project — create broader-scope models with finer-grained simulations and higher-quality rendering. It's one individual part today, but will it be an entire assembly tomorrow? As with all IT buying decisions, consider GPU performance that will scale into a future filled with more complex models, projects, and workflows.

About the Author: Alex Herrera

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